Many scientific, engineering, medical, and other technologies seek to identify the presence of an object within a medium. For example, some technologies detect the presence of buried landmines in a roadway or a field for military or humanitarian purposes. Such technologies may use ultra wideband ground-penetrating radar (“GPR”) antennas that are mounted on the front of a vehicle that travels on the roadway or across the field. The antennas are directed into the ground with the soil being the medium and the top of the soil or pavement being the surface. GPR systems can be used to detect not only metallic objects but also non-metallic objects whose dielectric properties are sufficiently different from those of the soil. When a radar signal strikes a subsurface object, it is reflected back as a return signal to a receiver. Current GPR systems typically analyze the strength or amplitude of the return signals directly to identify the presence of the object. Some GPR systems may, however, generate tomography images from the return signals. In the medical field, computer-assisted tomography uses X-rays to generate tomography images for detecting the presence of abnormalities (i.e., subsurface objects) within a body. In the engineering field, GPR systems have been designed to generate spatial images of the interior of concrete structures such as bridges, dams, and containment vessels to assist in assessing the integrity of the structures. In such images, the subsurface objects represented by such images tend to appear as distinct bright spots. In addition to referring to a foreign object that is within a medium, the term “object” also refers to any characteristic of the medium (e.g., crack in the medium and change in medium density) that is to be detected.
Using current imaging techniques, computational systems attached to arrays that contain dozens of antennas are unable to produce radar tomography images of the subsurface in real time. A real-time system needs to process the return signals from successive sampling locations of the vehicle as it travels so that, in the steady state, the return signals for one sampling location are processed within the time between samplings. Moreover, in the case of a vehicle that detects landmines, a real-time system may need to detect the presence of the landmine in time to stop the vehicle from hitting the landmine.
Once a subsurface object is detected, it can be helpful to distinguish between objects of different types or classes. For example, a GPR system may detect the presence of both landmines and large rocks that are below a roadway (e.g., in sandy soil). If the vehicle stops when any object is detected, then the vehicle would make many unneeded stops when traveling over, for example, a roadway over rocky soil or a roadway with various other non-hazardous subsurface objects (e.g., utility boxes, voids and cracks, and animal burrows). If the presence of landmines is relatively rare, then these unneeded stops may have a significant impact on the speed of detecting landmines (or other subsurface objects of interest). (The subsurface objects that are not of interest are referred to collectively as “clutter.”) Moreover, if a driver needs to decide whether to stop the vehicle each time an object is detected, the driver may become complacent when the vast majority of the detected objects are not objects of interest. Some approaches for classifying landmines have been proposed. These approaches may identify landmines based on geometric features, hidden Markov models, texture analysis, spatial pattern matching, and so on. Although these approaches have had some success in distinguishing landmines from clutter, these approaches have not been able to do so in real time. By distinguishing between objects of different types or classes (e.g., objects of interest and objects not of interest), a GPR system could provide more useful information for automatically or manually responding to a detected object in real time.